AquaticCLIP: A Vision-Language Foundation Model for Underwater Scene Analysis

• URL: http://arxiv.org/abs/2502.01785v1
• Date: Mon, 03 Feb 2025 19:56:16 GMT
• Title: AquaticCLIP: A Vision-Language Foundation Model for Underwater Scene Analysis
• Authors: Basit Alawode, Iyyakutti Iyappan Ganapathi, Sajid Javed, Naoufel Werghi, Mohammed Bennamoun, Arif Mahmood,
• Abstract summary: We introduce AquaticCLIP, a novel contrastive language-image pre-training model tailored for aquatic scene understanding.<n> AquaticCLIP presents a new unsupervised learning framework that aligns images and texts in aquatic environments.<n>Our model sets a new benchmark for vision-language applications in underwater environments.
• Score: 40.27548815196493
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The preservation of aquatic biodiversity is critical in mitigating the effects of climate change. Aquatic scene understanding plays a pivotal role in aiding marine scientists in their decision-making processes. In this paper, we introduce AquaticCLIP, a novel contrastive language-image pre-training model tailored for aquatic scene understanding. AquaticCLIP presents a new unsupervised learning framework that aligns images and texts in aquatic environments, enabling tasks such as segmentation, classification, detection, and object counting. By leveraging our large-scale underwater image-text paired dataset without the need for ground-truth annotations, our model enriches existing vision-language models in the aquatic domain. For this purpose, we construct a 2 million underwater image-text paired dataset using heterogeneous resources, including YouTube, Netflix, NatGeo, etc. To fine-tune AquaticCLIP, we propose a prompt-guided vision encoder that progressively aggregates patch features via learnable prompts, while a vision-guided mechanism enhances the language encoder by incorporating visual context. The model is optimized through a contrastive pretraining loss to align visual and textual modalities. AquaticCLIP achieves notable performance improvements in zero-shot settings across multiple underwater computer vision tasks, outperforming existing methods in both robustness and interpretability. Our model sets a new benchmark for vision-language applications in underwater environments. The code and dataset for AquaticCLIP are publicly available on GitHub at xxx.

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